论文标题
发散搜索几张图像分类
Divergent Search for Few-Shot Image Classification
论文作者
论文摘要
当数据未标记并且不知道目标任务时，Divergent搜索提供了学习广泛技能的策略。拥有这样的曲目可以使系统适应新的，无法预料的任务。未标记的图像数据很丰富，但是并不总是知道下游任务需要哪些功能。我们提出了一种在几片图像分类设置中进行分歧搜索的方法，并使用Omniglot和Mini-Imagenet进行评估。这个高维行为空间包括分区数据的所有可能方法。为了管理该领域的发散搜索，我们依靠一个元学习框架将各种任务中的有用功能集成到单个模型中。该模型的最后一层用作过去所有行为的“存档”的索引。我们在当前档案无法达到的行为空间中搜索区域。正如预期的那样，对评估任务的模型表现出色。但是它能够匹配，有时超出对目标任务或根本没有偏见的模型的性能。这表明即使在高维行为空间中，不同的搜索也是一种可行的方法。
When data is unlabelled and the target task is not known a priori, divergent search offers a strategy for learning a wide range of skills. Having such a repertoire allows a system to adapt to new, unforeseen tasks. Unlabelled image data is plentiful, but it is not always known which features will be required for downstream tasks. We propose a method for divergent search in the few-shot image classification setting and evaluate with Omniglot and Mini-ImageNet. This high-dimensional behavior space includes all possible ways of partitioning the data. To manage divergent search in this space, we rely on a meta-learning framework to integrate useful features from diverse tasks into a single model. The final layer of this model is used as an index into the `archive' of all past behaviors. We search for regions in the behavior space that the current archive cannot reach. As expected, divergent search is outperformed by models with a strong bias toward the evaluation tasks. But it is able to match and sometimes exceed the performance of models that have a weak bias toward the target task or none at all. This demonstrates that divergent search is a viable approach, even in high-dimensional behavior spaces.
